Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations369
Missing cells233
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory184.1 KiB
Average record size in memory511.0 B

Variable types

Text4
Categorical2
Numeric19

Alerts

cost_of_living_index is highly overall correlated with cost_of_living_plus_rent_index and 11 other fieldsHigh correlation
cost_of_living_plus_rent_index is highly overall correlated with cost_of_living_index and 13 other fieldsHigh correlation
country_encoded is highly overall correlated with cost_of_living_index and 9 other fieldsHigh correlation
duration_years is highly overall correlated with levelHigh correlation
exchange_rate is highly overall correlated with insurance_usd and 1 other fieldsHigh correlation
groceries_index is highly overall correlated with cost_of_living_index and 11 other fieldsHigh correlation
insurance_usd is highly overall correlated with cost_of_living_index and 9 other fieldsHigh correlation
level is highly overall correlated with duration_yearsHigh correlation
living_cost_index is highly overall correlated with cost_of_living_index and 8 other fieldsHigh correlation
local_purchasing_power_index is highly overall correlated with cost_of_living_index and 8 other fieldsHigh correlation
no_of_indian_students is highly overall correlated with cost_of_living_index and 9 other fieldsHigh correlation
overall_score is highly overall correlated with rent_usdHigh correlation
percentage is highly overall correlated with cost_of_living_index and 9 other fieldsHigh correlation
rank is highly overall correlated with cost_of_living_index and 11 other fieldsHigh correlation
rent_index is highly overall correlated with cost_of_living_index and 13 other fieldsHigh correlation
rent_usd is highly overall correlated with cost_of_living_index and 14 other fieldsHigh correlation
restaurant_price_index is highly overall correlated with cost_of_living_index and 9 other fieldsHigh correlation
total_cost is highly overall correlated with cost_of_living_plus_rent_index and 7 other fieldsHigh correlation
tuition_usd is highly overall correlated with cost_of_living_plus_rent_index and 7 other fieldsHigh correlation
visa_fee_usd is highly overall correlated with total_cost and 1 other fieldsHigh correlation
no_of_indian_students has 53 (14.4%) missing valuesMissing
percentage has 53 (14.4%) missing valuesMissing
country_encoded has 53 (14.4%) missing valuesMissing
overall_score has 74 (20.1%) missing valuesMissing
duration_years has 12 (3.3%) zerosZeros
tuition_usd has 32 (8.7%) zerosZeros

Reproduction

Analysis started2025-10-24 06:38:51.672611
Analysis finished2025-10-24 06:39:22.154171
Duration30.48 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

country
Text

Distinct55
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size21.0 KiB
2025-10-24T12:09:22.272603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.9512195
Min length4

Characters and Unicode

Total characters3303
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)4.1%

Sample

1st rowUnited States
2nd rowUnited Kingdom
3rd rowCanada
4th rowGermany
5th rowNetherlands
ValueCountFrequency (%)
united106
21.9%
kingdom69
14.3%
canada51
 
10.5%
states37
 
7.6%
australia28
 
5.8%
denmark11
 
2.3%
china10
 
2.1%
netherlands10
 
2.1%
japan10
 
2.1%
italy9
 
1.9%
Other values (50)143
29.5%
2025-10-24T12:09:22.503823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a445
13.5%
n363
 
11.0%
i292
 
8.8%
d284
 
8.6%
e253
 
7.7%
t248
 
7.5%
r116
 
3.5%
115
 
3.5%
U107
 
3.2%
o107
 
3.2%
Other values (35)973
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a445
13.5%
n363
 
11.0%
i292
 
8.8%
d284
 
8.6%
e253
 
7.7%
t248
 
7.5%
r116
 
3.5%
115
 
3.5%
U107
 
3.2%
o107
 
3.2%
Other values (35)973
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a445
13.5%
n363
 
11.0%
i292
 
8.8%
d284
 
8.6%
e253
 
7.7%
t248
 
7.5%
r116
 
3.5%
115
 
3.5%
U107
 
3.2%
o107
 
3.2%
Other values (35)973
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a445
13.5%
n363
 
11.0%
i292
 
8.8%
d284
 
8.6%
e253
 
7.7%
t248
 
7.5%
r116
 
3.5%
115
 
3.5%
U107
 
3.2%
o107
 
3.2%
Other values (35)973
29.5%

city
Text

Distinct227
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
2025-10-24T12:09:22.720768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.498645
Min length4

Characters and Unicode

Total characters2767
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)42.5%

Sample

1st rowCambridge
2nd rowLondon
3rd rowToronto
4th rowMunich
5th rowAmsterdam
ValueCountFrequency (%)
london8
 
2.0%
seattle6
 
1.5%
melbourne6
 
1.5%
leeds5
 
1.3%
wollongong5
 
1.3%
gold5
 
1.3%
coast5
 
1.3%
hobart5
 
1.3%
oxford5
 
1.3%
toronto4
 
1.0%
Other values (234)342
86.4%
2025-10-24T12:09:23.033656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a284
 
10.3%
o245
 
8.9%
e230
 
8.3%
n225
 
8.1%
r176
 
6.4%
i151
 
5.5%
l139
 
5.0%
t138
 
5.0%
s102
 
3.7%
g92
 
3.3%
Other values (44)985
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a284
 
10.3%
o245
 
8.9%
e230
 
8.3%
n225
 
8.1%
r176
 
6.4%
i151
 
5.5%
l139
 
5.0%
t138
 
5.0%
s102
 
3.7%
g92
 
3.3%
Other values (44)985
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a284
 
10.3%
o245
 
8.9%
e230
 
8.3%
n225
 
8.1%
r176
 
6.4%
i151
 
5.5%
l139
 
5.0%
t138
 
5.0%
s102
 
3.7%
g92
 
3.3%
Other values (44)985
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a284
 
10.3%
o245
 
8.9%
e230
 
8.3%
n225
 
8.1%
r176
 
6.4%
i151
 
5.5%
l139
 
5.0%
t138
 
5.0%
s102
 
3.7%
g92
 
3.3%
Other values (44)985
35.6%
Distinct228
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
2025-10-24T12:09:23.273133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length28
Mean length21.257453
Min length4

Characters and Unicode

Total characters7844
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)40.9%

Sample

1st rowHarvard University
2nd rowImperial College London
3rd rowUniversity of Toronto
4th rowTechnical University of Munich
5th rowUniversity of Amsterdam
ValueCountFrequency (%)
university339
33.2%
of195
19.1%
de12
 
1.2%
universidad10
 
1.0%
london7
 
0.7%
technology7
 
0.7%
columbia7
 
0.7%
washington6
 
0.6%
state6
 
0.6%
college6
 
0.6%
Other values (262)425
41.7%
2025-10-24T12:09:23.599942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i910
 
11.6%
651
 
8.3%
e635
 
8.1%
n629
 
8.0%
r542
 
6.9%
t511
 
6.5%
s478
 
6.1%
o461
 
5.9%
y376
 
4.8%
v374
 
4.8%
Other values (49)2277
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)7844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i910
 
11.6%
651
 
8.3%
e635
 
8.1%
n629
 
8.0%
r542
 
6.9%
t511
 
6.5%
s478
 
6.1%
o461
 
5.9%
y376
 
4.8%
v374
 
4.8%
Other values (49)2277
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i910
 
11.6%
651
 
8.3%
e635
 
8.1%
n629
 
8.0%
r542
 
6.9%
t511
 
6.5%
s478
 
6.1%
o461
 
5.9%
y376
 
4.8%
v374
 
4.8%
Other values (49)2277
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i910
 
11.6%
651
 
8.3%
e635
 
8.1%
n629
 
8.0%
r542
 
6.9%
t511
 
6.5%
s478
 
6.1%
o461
 
5.9%
y376
 
4.8%
v374
 
4.8%
Other values (49)2277
29.0%

program
Categorical

Distinct43
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size23.7 KiB
Computer Science
151 
Data Science
41 
Computer Engineering
22 
Software Engineering
21 
Information Systems
19 
Other values (38)
115 

Length

Max length23
Median length22
Mean length16.355014
Min length6

Characters and Unicode

Total characters6035
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)6.2%

Sample

1st rowComputer Science
2nd rowData Science
3rd rowBusiness Analytics
4th rowMechanical Engineering
5th rowArtificial Intelligence

Common Values

ValueCountFrequency (%)
Computer Science151
40.9%
Data Science41
 
11.1%
Computer Engineering22
 
6.0%
Software Engineering21
 
5.7%
Information Systems19
 
5.1%
Artificial Intelligence16
 
4.3%
Data Analytics16
 
4.3%
Information Technology10
 
2.7%
Software Development9
 
2.4%
Data Engineering8
 
2.2%
Other values (33)56
 
15.2%

Length

2025-10-24T12:09:23.691078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
science201
28.4%
computer173
24.5%
data65
 
9.2%
engineering59
 
8.3%
software31
 
4.4%
information30
 
4.2%
systems21
 
3.0%
analytics18
 
2.5%
artificial16
 
2.3%
intelligence16
 
2.3%
Other values (32)77
 
10.9%

Most occurring characters

ValueCountFrequency (%)
e878
14.5%
n550
 
9.1%
c507
 
8.4%
i490
 
8.1%
t421
 
7.0%
338
 
5.6%
r331
 
5.5%
o330
 
5.5%
a261
 
4.3%
m260
 
4.3%
Other values (27)1669
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6035
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e878
14.5%
n550
 
9.1%
c507
 
8.4%
i490
 
8.1%
t421
 
7.0%
338
 
5.6%
r331
 
5.5%
o330
 
5.5%
a261
 
4.3%
m260
 
4.3%
Other values (27)1669
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6035
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e878
14.5%
n550
 
9.1%
c507
 
8.4%
i490
 
8.1%
t421
 
7.0%
338
 
5.6%
r331
 
5.5%
o330
 
5.5%
a261
 
4.3%
m260
 
4.3%
Other values (27)1669
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6035
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e878
14.5%
n550
 
9.1%
c507
 
8.4%
i490
 
8.1%
t421
 
7.0%
338
 
5.6%
r331
 
5.5%
o330
 
5.5%
a261
 
4.3%
m260
 
4.3%
Other values (27)1669
27.7%

level
Categorical

High correlation 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
Master
182 
Bachelor
134 
PhD
53 

Length

Max length8
Median length6
Mean length6.295393
Min length3

Characters and Unicode

Total characters2323
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowMaster
3rd rowMaster
4th rowMaster
5th rowMaster

Common Values

ValueCountFrequency (%)
Master182
49.3%
Bachelor134
36.3%
PhD53
 
14.4%

Length

2025-10-24T12:09:23.786621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T12:09:23.842989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
master182
49.3%
bachelor134
36.3%
phd53
 
14.4%

Most occurring characters

ValueCountFrequency (%)
a316
13.6%
e316
13.6%
r316
13.6%
h187
8.0%
M182
7.8%
t182
7.8%
s182
7.8%
B134
5.8%
c134
5.8%
l134
5.8%
Other values (3)240
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a316
13.6%
e316
13.6%
r316
13.6%
h187
8.0%
M182
7.8%
t182
7.8%
s182
7.8%
B134
5.8%
c134
5.8%
l134
5.8%
Other values (3)240
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a316
13.6%
e316
13.6%
r316
13.6%
h187
8.0%
M182
7.8%
t182
7.8%
s182
7.8%
B134
5.8%
c134
5.8%
l134
5.8%
Other values (3)240
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a316
13.6%
e316
13.6%
r316
13.6%
h187
8.0%
M182
7.8%
t182
7.8%
s182
7.8%
B134
5.8%
c134
5.8%
l134
5.8%
Other values (3)240
10.3%

duration_years
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4549458
Minimum0
Maximum1
Zeros12
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:23.899581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.5
Q30.75
95-th percentile0.75
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.23593793
Coefficient of variation (CV)0.51860669
Kurtosis-1.1195586
Mean0.4549458
Median Absolute Deviation (MAD)0.25
Skewness0.2535046
Sum167.875
Variance0.055666709
MonotonicityNot monotonic
2025-10-24T12:09:23.965540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.25160
43.4%
0.75107
29.0%
0.581
22.0%
012
 
3.3%
16
 
1.6%
0.3753
 
0.8%
ValueCountFrequency (%)
012
 
3.3%
0.25160
43.4%
0.3753
 
0.8%
0.581
22.0%
0.75107
29.0%
16
 
1.6%
ValueCountFrequency (%)
16
 
1.6%
0.75107
29.0%
0.581
22.0%
0.3753
 
0.8%
0.25160
43.4%
012
 
3.3%

tuition_usd
Real number (ℝ)

High correlation  Zeros 

Distinct192
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32289099
Minimum0
Maximum1
Zeros32
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:24.054971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.061290323
median0.32580645
Q30.53064516
95-th percentile0.83451613
Maximum1
Range1
Interquartile range (IQR)0.46935484

Descriptive statistics

Standard deviation0.27053653
Coefficient of variation (CV)0.83785718
Kurtosis-0.94626868
Mean0.32289099
Median Absolute Deviation (MAD)0.25322581
Skewness0.44389986
Sum119.14677
Variance0.073190015
MonotonicityNot monotonic
2025-10-24T12:09:24.162056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
8.7%
0.05645161297
 
1.9%
0.50322580657
 
1.9%
0.061290322587
 
1.9%
0.024193548396
 
1.6%
0.064516129036
 
1.6%
0.54838709686
 
1.6%
0.072580645165
 
1.4%
0.45967741945
 
1.4%
0.5645161295
 
1.4%
Other values (182)283
76.7%
ValueCountFrequency (%)
032
8.7%
0.0064516129031
 
0.3%
0.0080645161291
 
0.3%
0.016129032261
 
0.3%
0.019354838711
 
0.3%
0.022258064521
 
0.3%
0.022903225811
 
0.3%
0.023225806451
 
0.3%
0.02354838711
 
0.3%
0.024193548396
 
1.6%
ValueCountFrequency (%)
12
0.5%
0.9354838713
0.8%
0.91935483871
 
0.3%
0.89354838711
 
0.3%
0.88709677421
 
0.3%
0.87903225811
 
0.3%
0.87419354841
 
0.3%
0.87096774193
0.8%
0.86290322581
 
0.3%
0.85161290321
 
0.3%

living_cost_index
Real number (ℝ)

High correlation 

Distinct146
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41025972
Minimum0.0073995772
Maximum0.97251586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:24.275791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0073995772
5-th percentile0.1627907
Q10.35623679
median0.43023256
Q30.4756871
95-th percentile0.59725159
Maximum0.97251586
Range0.96511628
Interquartile range (IQR)0.11945032

Descriptive statistics

Standard deviation0.13779887
Coefficient of variation (CV)0.33588204
Kurtosis1.8162625
Mean0.41025972
Median Absolute Deviation (MAD)0.05602537
Skewness0.14096211
Sum151.38584
Variance0.018988529
MonotonicityNot monotonic
2025-10-24T12:09:24.378027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.458773784416
 
4.3%
0.4439746311
 
3.0%
0.397463002110
 
2.7%
0.401691331910
 
2.7%
0.434460887910
 
2.7%
0.58562367869
 
2.4%
0.43023255818
 
2.2%
0.387949268
 
2.2%
0.44820295987
 
1.9%
0.52854122627
 
1.9%
Other values (136)273
74.0%
ValueCountFrequency (%)
0.0073995771671
 
0.3%
0.04228329811
 
0.3%
0.078224101481
 
0.3%
0.095137420721
 
0.3%
0.10676532771
 
0.3%
0.11310782244
1.1%
0.11733615221
 
0.3%
0.12684989431
 
0.3%
0.13107822412
0.5%
0.14164904861
 
0.3%
ValueCountFrequency (%)
0.97251585621
 
0.3%
0.93763213531
 
0.3%
0.91437632141
 
0.3%
0.87315010571
 
0.3%
0.76321353073
0.8%
0.71247357292
0.5%
0.66279069771
 
0.3%
0.65539112051
 
0.3%
0.65221987321
 
0.3%
0.63107822411
 
0.3%

rent_usd
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39554864
Minimum0.0085106383
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:24.478721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0085106383
5-th percentile0.065531915
Q10.25531915
median0.36170213
Q30.53191489
95-th percentile0.78723404
Maximum1
Range0.99148936
Interquartile range (IQR)0.27659574

Descriptive statistics

Standard deviation0.2202931
Coefficient of variation (CV)0.55693051
Kurtosis-0.26656051
Mean0.39554864
Median Absolute Deviation (MAD)0.12765957
Skewness0.46724562
Sum145.95745
Variance0.048529052
MonotonicityNot monotonic
2025-10-24T12:09:24.584351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.446808510630
 
8.1%
0.319148936224
 
6.5%
0.340425531922
 
6.0%
0.361702127720
 
5.4%
0.489361702120
 
5.4%
0.404255319118
 
4.9%
0.276595744715
 
4.1%
0.531914893615
 
4.1%
0.702127659614
 
3.8%
0.297872340412
 
3.3%
Other values (39)179
48.5%
ValueCountFrequency (%)
0.0085106382981
 
0.3%
0.012765957451
 
0.3%
0.021276595741
 
0.3%
0.029787234041
 
0.3%
0.042553191494
1.1%
0.055319148943
0.8%
0.059574468091
 
0.3%
0.063829787237
1.9%
0.068085106381
 
0.3%
0.072340425532
 
0.5%
ValueCountFrequency (%)
13
 
0.8%
0.95744680852
 
0.5%
0.9148936173
 
0.8%
0.87234042554
 
1.1%
0.8297872343
 
0.8%
0.78723404269
2.4%
0.744680851110
2.7%
0.72340425531
 
0.3%
0.702127659614
3.8%
0.659574468110
2.7%

visa_fee_usd
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46752785
Minimum0
Maximum1
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:24.681076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.088888889
Q10.22222222
median0.31111111
Q30.91111111
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.68888889

Descriptive statistics

Standard deviation0.33639232
Coefficient of variation (CV)0.71951289
Kurtosis-1.242584
Mean0.46752785
Median Absolute Deviation (MAD)0.15555556
Skewness0.60531416
Sum172.51778
Variance0.11315979
MonotonicityNot monotonic
2025-10-24T12:09:24.766203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.433333333351
13.8%
0.266666666743
11.7%
138
10.3%
0.911111111134
9.2%
0.988888888931
 
8.4%
0.0888888888925
 
6.8%
0.222222222222
 
6.0%
0.177777777820
 
5.4%
0.133333333313
 
3.5%
0.244444444413
 
3.5%
Other values (16)79
21.4%
ValueCountFrequency (%)
02
 
0.5%
0.077777777785
 
1.4%
0.0888888888925
6.8%
0.10666666674
 
1.1%
0.11111111118
 
2.2%
0.13111111118
 
2.2%
0.133333333313
3.5%
0.15555555565
 
1.4%
0.177777777820
5.4%
0.21
 
0.3%
ValueCountFrequency (%)
138
10.3%
0.988888888931
8.4%
0.911111111134
9.2%
0.68888888898
 
2.2%
0.64444444443
 
0.8%
0.52222222223
 
0.8%
0.46666666671
 
0.3%
0.433333333351
13.8%
0.410
 
2.7%
0.35555555564
 
1.1%

insurance_usd
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41667709
Minimum0
Maximum1
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:24.845661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.083076923
Q10.30769231
median0.4
Q30.46153846
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.15384615

Descriptive statistics

Standard deviation0.24106679
Coefficient of variation (CV)0.57854581
Kurtosis1.1121616
Mean0.41667709
Median Absolute Deviation (MAD)0.092307692
Skewness1.0998888
Sum153.75385
Variance0.058113195
MonotonicityNot monotonic
2025-10-24T12:09:24.920653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.461538461585
23.0%
0.307692307743
11.7%
0.346153846240
10.8%
137
10.0%
0.423076923132
 
8.7%
0.153846153831
 
8.4%
0.538461538515
 
4.1%
0.384615384612
 
3.3%
0.115384615410
 
2.7%
0.192307692310
 
2.7%
Other values (10)54
14.6%
ValueCountFrequency (%)
03
 
0.8%
0.038461538464
 
1.1%
0.061538461544
 
1.1%
0.076923076928
 
2.2%
0.092307692313
 
0.8%
0.115384615410
 
2.7%
0.153846153831
8.4%
0.192307692310
 
2.7%
0.23076923086
 
1.6%
0.26923076922
 
0.5%
ValueCountFrequency (%)
137
10.0%
0.76923076928
 
2.2%
0.538461538515
 
4.1%
0.56
 
1.6%
0.461538461585
23.0%
0.423076923132
 
8.7%
0.410
 
2.7%
0.384615384612
 
3.3%
0.346153846240
10.8%
0.307692307743
11.7%

exchange_rate
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016913465
Minimum0
Maximum1
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:25.007487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5183921 × 10-5
Q11.8268155 × 10-5
median2.0166145 × 10-5
Q38.5409557 × 10-5
95-th percentile0.0034555283
Maximum1
Range1
Interquartile range (IQR)6.7141401 × 10-5

Descriptive statistics

Standard deviation0.11163181
Coefficient of variation (CV)6.6001736
Kurtosis64.72963
Mean0.016913465
Median Absolute Deviation (MAD)4.9822241 × 10-6
Skewness7.87622
Sum6.2410687
Variance0.012461661
MonotonicityNot monotonic
2025-10-24T12:09:25.115896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1.518392118 × 10-569
18.7%
1.826815517 × 10-569
18.7%
2.846985221 × 10-552
14.1%
2.016614531 × 10-539
10.6%
3.250308127 × 10-528
 
7.6%
0.00345552831110
 
2.7%
0.00016607413799
 
2.4%
0.00024436623148
 
2.2%
1.755640886 × 10-58
 
2.2%
0.00017319160096
 
1.6%
Other values (33)71
19.2%
ValueCountFrequency (%)
03
 
0.8%
3.795980294 × 10-61
 
0.3%
5.456721673 × 10-61
 
0.3%
1.518392118 × 10-569
18.7%
1.755640886 × 10-58
 
2.2%
1.826815517 × 10-569
18.7%
2.016614531 × 10-539
10.6%
2.823260344 × 10-51
 
0.3%
2.846985221 × 10-552
14.1%
3.250308127 × 10-528
7.6%
ValueCountFrequency (%)
14
 
1.1%
0.37010451993
 
0.8%
0.35586959382
 
0.5%
0.093709704783
 
0.8%
0.020404580321
 
0.3%
0.0083060793814
 
1.1%
0.00345552831110
2.7%
0.0032586118341
 
0.3%
0.0025943152821
 
0.3%
0.0025705904053
 
0.8%

total_cost
Real number (ℝ)

High correlation 

Distinct352
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21131859
Minimum0.0024376009
Maximum0.97253407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:25.218664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0024376009
5-th percentile0.0089332921
Q10.036594912
median0.14898548
Q30.33704123
95-th percentile0.57396917
Maximum0.97253407
Range0.97009647
Interquartile range (IQR)0.30044632

Descriptive statistics

Standard deviation0.20245103
Coefficient of variation (CV)0.958037
Kurtosis1.3741003
Mean0.21131859
Median Absolute Deviation (MAD)0.12454767
Skewness1.1991818
Sum77.976561
Variance0.040986421
MonotonicityNot monotonic
2025-10-24T12:09:25.322739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.89528616062
 
0.5%
0.065746558182
 
0.5%
0.24786280772
 
0.5%
0.0055275174242
 
0.5%
0.030178185192
 
0.5%
0.25678923342
 
0.5%
0.44553163732
 
0.5%
0.043461393212
 
0.5%
0.028255570432
 
0.5%
0.93476842792
 
0.5%
Other values (342)349
94.6%
ValueCountFrequency (%)
0.0024376008511
0.3%
0.002643595292
0.5%
0.0045662100461
0.3%
0.0055275174242
0.5%
0.0057678442681
0.3%
0.0062141655511
0.3%
0.0065574896141
0.3%
0.0067291516461
0.3%
0.0074157997731
0.3%
0.0075874618051
0.3%
ValueCountFrequency (%)
0.97253407491
0.3%
0.93476842792
0.5%
0.89528616062
0.5%
0.88498643871
0.3%
0.81838157041
0.3%
0.77992927521
0.3%
0.77168949771
0.3%
0.76344972021
0.3%
0.73186390631
0.3%
0.72225083261
0.3%

rank
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.99187
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:25.421532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q111
median22
Q338
95-th percentile89
Maximum119
Range118
Interquartile range (IQR)27

Descriptive statistics

Standard deviation27.21787
Coefficient of variation (CV)0.93881043
Kurtosis1.8555797
Mean28.99187
Median Absolute Deviation (MAD)12
Skewness1.644612
Sum10698
Variance740.81243
MonotonicityNot monotonic
2025-10-24T12:09:25.526126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2269
18.7%
1251
13.8%
937
 
10.0%
1028
 
7.6%
711
 
3.0%
8510
 
2.7%
1810
 
2.7%
4610
 
2.7%
269
 
2.4%
179
 
2.4%
Other values (45)125
33.9%
ValueCountFrequency (%)
18
 
2.2%
31
 
0.3%
42
 
0.5%
64
 
1.1%
711
 
3.0%
937
10.0%
1028
7.6%
112
 
0.5%
1251
13.8%
133
 
0.8%
ValueCountFrequency (%)
1192
0.5%
1182
0.5%
1171
 
0.3%
1084
1.1%
1073
0.8%
1023
0.8%
951
 
0.3%
901
 
0.3%
894
1.1%
861
 
0.3%

cost_of_living_index
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48750852
Minimum0.02673147
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:25.639747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02673147
5-th percentile0.14823815
Q10.37788578
median0.52490887
Q30.56257594
95-th percentile0.65006075
Maximum1
Range0.97326853
Interquartile range (IQR)0.18469016

Descriptive statistics

Standard deviation0.17735707
Coefficient of variation (CV)0.36380301
Kurtosis0.9000732
Mean0.48750852
Median Absolute Deviation (MAD)0.09963548
Skewness-0.43494929
Sum179.89064
Variance0.03145553
MonotonicityNot monotonic
2025-10-24T12:09:25.749307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5249088769
18.7%
0.558930741251
13.8%
0.626974483637
 
10.0%
0.624544349928
 
7.6%
0.650060753311
 
3.0%
0.156743620910
 
2.7%
0.538274605110
 
2.7%
0.331713244210
 
2.7%
0.45443499399
 
2.4%
0.53948967199
 
2.4%
Other values (45)125
33.9%
ValueCountFrequency (%)
0.026731470232
0.5%
0.029161603892
0.5%
0.044957472661
 
0.3%
0.092345078984
1.1%
0.095990279473
0.8%
0.12150668293
0.8%
0.13608748481
 
0.3%
0.14702308631
 
0.3%
0.14823815314
1.1%
0.15309842041
 
0.3%
ValueCountFrequency (%)
18
 
2.2%
0.7800729041
 
0.3%
0.70352369382
 
0.5%
0.6950182264
 
1.1%
0.650060753311
 
3.0%
0.626974483637
10.0%
0.624544349928
7.6%
0.56257594172
 
0.5%
0.558930741251
13.8%
0.55650060753
 
0.8%

rent_index
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38225601
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:25.854482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11574074
Q10.25925926
median0.44290123
Q30.47530864
95-th percentile0.60648148
Maximum1
Range1
Interquartile range (IQR)0.21604938

Descriptive statistics

Standard deviation0.16624969
Coefficient of variation (CV)0.43491714
Kurtosis-0.055750205
Mean0.38225601
Median Absolute Deviation (MAD)0.125
Skewness-0.06342065
Sum141.05247
Variance0.02763896
MonotonicityNot monotonic
2025-10-24T12:09:25.956481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.442901234669
18.7%
0.47530864251
13.8%
0.606481481537
 
10.0%
0.478395061728
 
7.6%
0.169753086412
 
3.3%
0.370370370411
 
3.0%
0.479938271610
 
2.7%
0.152777777810
 
2.7%
0.26697530869
 
2.4%
0.25925925939
 
2.4%
Other values (40)123
33.3%
ValueCountFrequency (%)
01
 
0.3%
0.02006172842
0.5%
0.049382716052
0.5%
0.081790123463
0.8%
0.094135802474
1.1%
0.095679012354
1.1%
0.11111111111
 
0.3%
0.11574074073
0.8%
0.11728395064
1.1%
0.12037037041
 
0.3%
ValueCountFrequency (%)
12
 
0.5%
0.68055555568
 
2.2%
0.61574074073
 
0.8%
0.606481481537
10.0%
0.5879629631
 
0.3%
0.56790123461
 
0.3%
0.479938271610
 
2.7%
0.478395061728
7.6%
0.47530864251
13.8%
0.442901234669
18.7%

cost_of_living_plus_rent_index
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51036861
Minimum0.02507837
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:26.065747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02507837
5-th percentile0.15360502
Q10.38401254
median0.56426332
Q30.61285266
95-th percentile0.71316614
Maximum1
Range0.97492163
Interquartile range (IQR)0.22884013

Descriptive statistics

Standard deviation0.19279463
Coefficient of variation (CV)0.37775566
Kurtosis0.19206769
Mean0.51036861
Median Absolute Deviation (MAD)0.084639498
Skewness-0.42558605
Sum188.32602
Variance0.03716977
MonotonicityNot monotonic
2025-10-24T12:09:26.174047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.564263322969
18.7%
0.603448275951
13.8%
0.713166144237
 
10.0%
0.648902821328
 
7.6%
0.612852664611
 
3.0%
0.177115987510
 
2.7%
0.59247648910
 
2.7%
0.30250783710
 
2.7%
0.42946708469
 
2.4%
0.49059561139
 
2.4%
Other values (45)125
33.9%
ValueCountFrequency (%)
0.025078369912
0.5%
0.026645768031
 
0.3%
0.04075235112
0.5%
0.10188087773
0.8%
0.1050156744
1.1%
0.12539184953
0.8%
0.14263322881
 
0.3%
0.15360501574
1.1%
0.15517241381
 
0.3%
0.15673981191
 
0.3%
ValueCountFrequency (%)
18
 
2.2%
0.95611285272
 
0.5%
0.79780564261
 
0.3%
0.713166144237
10.0%
0.66927899693
 
0.8%
0.648902821328
7.6%
0.64263322884
 
1.1%
0.63949843261
 
0.3%
0.612852664611
 
3.0%
0.603448275951
13.8%

groceries_index
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46456551
Minimum0.040393013
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:26.277547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.040393013
5-th percentile0.16048035
Q10.39519651
median0.45633188
Q30.58624454
95-th percentile0.65283843
Maximum1
Range0.95960699
Interquartile range (IQR)0.19104803

Descriptive statistics

Standard deviation0.1774156
Coefficient of variation (CV)0.38189576
Kurtosis0.61947729
Mean0.46456551
Median Absolute Deviation (MAD)0.12991266
Skewness0.0031897507
Sum171.42467
Variance0.031476295
MonotonicityNot monotonic
2025-10-24T12:09:26.379156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.430131004469
18.7%
0.586244541551
13.8%
0.627729257637
 
10.0%
0.652838427928
 
7.6%
0.2106986913
 
3.5%
0.516375545911
 
3.0%
0.462882096110
 
2.7%
0.395196506610
 
2.7%
0.43122270749
 
2.4%
0.49672489089
 
2.4%
Other values (43)122
33.1%
ValueCountFrequency (%)
0.04039301312
0.5%
0.049126637554
1.1%
0.068777292582
0.5%
0.089519650661
 
0.3%
0.12336244544
1.1%
0.13318777291
 
0.3%
0.13973799133
0.8%
0.15283842791
 
0.3%
0.16048034932
0.5%
0.1735807863
0.8%
ValueCountFrequency (%)
18
 
2.2%
0.77401746721
 
0.3%
0.67139737994
 
1.1%
0.652838427928
7.6%
0.627729257637
10.0%
0.62336244542
 
0.5%
0.59388646293
 
0.8%
0.586244541551
13.8%
0.56768558958
 
2.2%
0.53384279482
 
0.5%

restaurant_price_index
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50186676
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:26.490176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.091448931
Q10.37173397
median0.5807601
Q30.59263658
95-th percentile0.81353919
Maximum1
Range1
Interquartile range (IQR)0.22090261

Descriptive statistics

Standard deviation0.20725244
Coefficient of variation (CV)0.41296308
Kurtosis0.28917607
Mean0.50186676
Median Absolute Deviation (MAD)0.064133017
Skewness-0.68103409
Sum185.18884
Variance0.042953576
MonotonicityNot monotonic
2025-10-24T12:09:26.601768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.592636579669
18.7%
0.58076009551
13.8%
0.646080760137
 
10.0%
0.590261282728
 
7.6%
0.813539192411
 
3.0%
0.0926365795711
 
3.0%
0.574821852710
 
2.7%
0.178147268410
 
2.7%
0.60332541579
 
2.4%
0.48099762479
 
2.4%
Other values (43)124
33.6%
ValueCountFrequency (%)
01
 
0.3%
0.0083135391923
 
0.8%
0.027315914492
 
0.5%
0.040380047512
 
0.5%
0.041567695964
 
1.1%
0.065320665086
1.6%
0.091448931123
 
0.8%
0.0926365795711
3.0%
0.10095011881
 
0.3%
0.13895486941
 
0.3%
ValueCountFrequency (%)
18
 
2.2%
0.87885985751
 
0.3%
0.813539192411
 
3.0%
0.72090261284
 
1.1%
0.66389548691
 
0.3%
0.65558194771
 
0.3%
0.646080760137
10.0%
0.63182897864
 
1.1%
0.62351543943
 
0.8%
0.60332541579
 
2.4%

local_purchasing_power_index
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56841991
Minimum0.048279689
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:26.703604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.048279689
5-th percentile0.2136515
Q10.49833518
median0.62652608
Q30.68035516
95-th percentile0.77691454
Maximum1
Range0.95172031
Interquartile range (IQR)0.18201998

Descriptive statistics

Standard deviation0.17459428
Coefficient of variation (CV)0.30715722
Kurtosis0.42962214
Mean0.56841991
Median Absolute Deviation (MAD)0.067702553
Skewness-0.90165344
Sum209.74695
Variance0.030483163
MonotonicityNot monotonic
2025-10-24T12:09:26.811237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.626526082169
18.7%
0.562708102151
13.8%
0.776914539437
 
10.0%
0.694228634928
 
7.6%
0.693118756911
 
3.0%
0.680355160910
 
2.7%
0.636514983410
 
2.7%
0.555493895710
 
2.7%
0.394006659310
 
2.7%
0.42175360719
 
2.4%
Other values (44)124
33.6%
ValueCountFrequency (%)
0.048279689231
 
0.3%
0.089345172031
 
0.3%
0.098224195342
 
0.5%
0.1281908992
 
0.5%
0.13152053274
1.1%
0.16315205333
0.8%
0.17092119871
 
0.3%
0.17425083243
0.8%
0.18035516091
 
0.3%
0.21365149836
1.6%
ValueCountFrequency (%)
11
 
0.3%
0.8851276361
 
0.3%
0.86792452838
 
2.2%
0.776914539437
10.0%
0.73584905664
 
1.1%
0.694228634928
7.6%
0.693118756911
 
3.0%
0.680355160910
 
2.7%
0.66759156494
 
1.1%
0.65871254163
 
0.8%

no_of_indian_students
Real number (ℝ)

High correlation  Missing 

Distinct35
Distinct (%)11.1%
Missing53
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean0.23125956
Minimum0
Maximum1
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:26.914676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00040532314
Q10.0060210828
median0.10073712
Q30.30134947
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.29532839

Descriptive statistics

Standard deviation0.30979917
Coefficient of variation (CV)1.3396167
Kurtosis1.6334641
Mean0.23125956
Median Absolute Deviation (MAD)0.098633654
Skewness1.6605288
Sum73.078021
Variance0.095975528
MonotonicityNot monotonic
2025-10-24T12:09:27.005509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.100737115569
18.7%
0.301349469951
13.8%
137
10.0%
0.403123248428
 
7.6%
0.00164540101411
 
3.0%
0.00602108283110
 
2.7%
0.100610546210
 
2.7%
0.00529180252810
 
2.7%
0.0070035017519
 
2.4%
0.0026760368148
 
2.2%
Other values (25)73
19.8%
(Missing)53
14.4%
ValueCountFrequency (%)
03
0.8%
6.027109941 × 10-62
 
0.5%
4.218976958 × 10-55
1.4%
5.424398946 × 10-51
 
0.3%
0.0001024608693
0.8%
0.00021094884792
 
0.5%
0.00047011457541
 
0.3%
0.00059668388412
 
0.5%
0.00065695498352
 
0.5%
0.0011993948783
0.8%
ValueCountFrequency (%)
137
10.0%
0.403123248428
7.6%
0.301349469951
13.8%
0.1928614913
 
0.8%
0.16272594131
 
0.3%
0.100737115569
18.7%
0.100610546210
 
2.7%
0.030129522598
 
2.2%
0.015061747743
 
0.8%
0.014097410154
 
1.1%

percentage
Real number (ℝ)

High correlation  Missing 

Distinct35
Distinct (%)11.1%
Missing53
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean0.23125956
Minimum0
Maximum1
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:27.094525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00040532314
Q10.0060210828
median0.10073712
Q30.30134947
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.29532839

Descriptive statistics

Standard deviation0.30979917
Coefficient of variation (CV)1.3396167
Kurtosis1.6334641
Mean0.23125956
Median Absolute Deviation (MAD)0.098633654
Skewness1.6605288
Sum73.078021
Variance0.095975528
MonotonicityNot monotonic
2025-10-24T12:09:27.185201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.100737115569
18.7%
0.301349469951
13.8%
137
10.0%
0.403123248428
 
7.6%
0.00164540101411
 
3.0%
0.00602108283110
 
2.7%
0.100610546210
 
2.7%
0.00529180252810
 
2.7%
0.0070035017519
 
2.4%
0.0026760368148
 
2.2%
Other values (25)73
19.8%
(Missing)53
14.4%
ValueCountFrequency (%)
03
0.8%
6.027109941 × 10-62
 
0.5%
4.218976958 × 10-55
1.4%
5.424398946 × 10-51
 
0.3%
0.0001024608693
0.8%
0.00021094884792
 
0.5%
0.00047011457541
 
0.3%
0.00059668388412
 
0.5%
0.00065695498352
 
0.5%
0.0011993948783
0.8%
ValueCountFrequency (%)
137
10.0%
0.403123248428
7.6%
0.301349469951
13.8%
0.1928614913
 
0.8%
0.16272594131
 
0.3%
0.100737115569
18.7%
0.100610546210
 
2.7%
0.030129522598
 
2.2%
0.015061747743
 
0.8%
0.014097410154
 
1.1%

country_encoded
Real number (ℝ)

High correlation  Missing 

Distinct37
Distinct (%)11.7%
Missing53
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean0.80165925
Minimum0
Maximum1
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:27.276781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28378378
Q10.63513514
median0.93243243
Q30.97297297
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.33783784

Descriptive statistics

Standard deviation0.24939208
Coefficient of variation (CV)0.31109487
Kurtosis1.0304216
Mean0.80165925
Median Absolute Deviation (MAD)0.054054054
Skewness-1.3941842
Sum253.32432
Variance0.06219641
MonotonicityNot monotonic
2025-10-24T12:09:27.385486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.932432432469
18.7%
0.97297297351
13.8%
137
10.0%
0.986486486528
 
7.6%
0.432432432411
 
3.0%
0.594594594610
 
2.7%
0.918918918910
 
2.7%
0.68918918929
 
2.4%
0.63513513519
 
2.4%
0.51351351358
 
2.2%
Other values (27)74
20.1%
(Missing)53
14.4%
ValueCountFrequency (%)
03
0.8%
0.054054054052
 
0.5%
0.10810810815
1.4%
0.13513513511
 
0.3%
0.18918918923
0.8%
0.24324324322
 
0.5%
0.29729729731
 
0.3%
0.31081081082
 
0.5%
0.32432432432
 
0.5%
0.39189189193
0.8%
ValueCountFrequency (%)
137
10.0%
0.986486486528
7.6%
0.97297297351
13.8%
0.95945945953
 
0.8%
0.94594594591
 
0.3%
0.932432432469
18.7%
0.918918918910
 
2.7%
0.85135135148
 
2.2%
0.81081081083
 
0.8%
0.78378378384
 
1.1%
Distinct166
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2025-10-24T12:09:27.652334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.5880759
Min length1

Characters and Unicode

Total characters1324
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)20.3%

Sample

1st row4
2nd row2
3rd row25
4th row28
5th row55
ValueCountFrequency (%)
1201-140011
 
3.0%
951-100010
 
2.7%
1001-12007
 
1.9%
1676
 
1.6%
766
 
1.6%
65
 
1.4%
805
 
1.4%
14015
 
1.4%
5355
 
1.4%
661-6705
 
1.4%
Other values (156)304
82.4%
2025-10-24T12:09:27.986517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1262
19.8%
0205
15.5%
5130
9.8%
2125
9.4%
6111
8.4%
492
 
6.9%
788
 
6.6%
386
 
6.5%
876
 
5.7%
975
 
5.7%
Other values (2)74
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1262
19.8%
0205
15.5%
5130
9.8%
2125
9.4%
6111
8.4%
492
 
6.9%
788
 
6.6%
386
 
6.5%
876
 
5.7%
975
 
5.7%
Other values (2)74
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1262
19.8%
0205
15.5%
5130
9.8%
2125
9.4%
6111
8.4%
492
 
6.9%
788
 
6.6%
386
 
6.5%
876
 
5.7%
975
 
5.7%
Other values (2)74
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1262
19.8%
0205
15.5%
5130
9.8%
2125
9.4%
6111
8.4%
492
 
6.9%
788
 
6.6%
386
 
6.5%
876
 
5.7%
975
 
5.7%
Other values (2)74
 
5.6%

overall_score
Real number (ℝ)

High correlation  Missing 

Distinct142
Distinct (%)48.1%
Missing74
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean0.41261342
Minimum0
Maximum0.98106061
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-10-24T12:09:28.076132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.026136364
Q10.20517677
median0.38510101
Q30.55997475
95-th percentile0.95075758
Maximum0.98106061
Range0.98106061
Interquartile range (IQR)0.35479798

Descriptive statistics

Standard deviation0.26520768
Coefficient of variation (CV)0.64275097
Kurtosis-0.68277875
Mean0.41261342
Median Absolute Deviation (MAD)0.17676768
Skewness0.416198
Sum121.72096
Variance0.070335112
MonotonicityNot monotonic
2025-10-24T12:09:28.180392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56186868696
 
1.6%
0.37247474756
 
1.6%
0.24368686875
 
1.4%
0.6255
 
1.4%
0.95075757585
 
1.4%
0.20454545455
 
1.4%
0.54671717175
 
1.4%
0.026515151525
 
1.4%
0.55808080814
 
1.1%
0.48989898994
 
1.1%
Other values (132)245
66.4%
(Missing)74
 
20.1%
ValueCountFrequency (%)
01
 
0.3%
0.0012626262631
 
0.3%
0.0050505050512
0.5%
0.0088383838381
 
0.3%
0.012626262631
 
0.3%
0.013888888892
0.5%
0.018939393941
 
0.3%
0.021464646461
 
0.3%
0.022727272733
0.8%
0.025252525252
0.5%
ValueCountFrequency (%)
0.98106060613
0.8%
0.96085858594
1.1%
0.95959595962
 
0.5%
0.95833333333
0.8%
0.95075757585
1.4%
0.87752525252
 
0.5%
0.85479797981
 
0.3%
0.84722222221
 
0.3%
0.82954545452
 
0.5%
0.82575757583
0.8%

Interactions

2025-10-24T12:09:20.432556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:52.650566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:55.417143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:58.166701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:00.212196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.478843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.774936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:04.083629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:05.378453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.676288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:08.077408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:09.368082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.703562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:12.499598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.834602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:15.185334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.503786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.837161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:19.117171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.505677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:52.792536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:55.558980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:58.326272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:00.276830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.546086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-24T12:09:18.573365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:19.884659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.239325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:54.402165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:57.156172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:59.667633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.014307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.308091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:03.607549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:04.906993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.198465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:07.561156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:08.894747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.185525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:11.583431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.368411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:14.668128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.004660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.359550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:18.640280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:19.952426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.303798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:54.546352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:57.312627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:59.753512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.079368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.374161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:03.675690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:04.975151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.264159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:07.641659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:08.961217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.264676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:11.654608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.435106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:14.744082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.070386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.426137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:18.714416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.019688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.370562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:54.692748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:57.454358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:59.846869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.145484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.441071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:03.743328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:05.042228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.331486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:07.714813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:09.038348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.335333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:11.726353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.502508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:14.820710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.138266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.491941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:18.782632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.094568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.447085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:54.836283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:57.598137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:59.925345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.218281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.505938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:03.811679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:05.107045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.398498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:07.790987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:09.105766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.414201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:11.808387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.567424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:14.897734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.202004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.565521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:18.857210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.169475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.514245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:54.979888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:57.742305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:00.002224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.283227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.578606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:03.877080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:05.173635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.465889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:07.863818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:09.171521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.489761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:12.285233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.640085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:14.972513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.277081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.630361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:18.923660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.235483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.585303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:55.121367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:57.884408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:00.073934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.348280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.643285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:03.951805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:05.239158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.531899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:07.934870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:09.236650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.562536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:12.360193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.704459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:15.046944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.355775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.697432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:18.988538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.301751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:21.650463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:55.260576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:08:58.027575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:00.144779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:01.412476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:02.710753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:04.017039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:05.314111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:06.598880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:08.002749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:09.302541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:10.635878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:12.429687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:13.771116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:15.119492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:16.437416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:17.762393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:19.054251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-24T12:09:20.368657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-24T12:09:28.284556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
cost_of_living_indexcost_of_living_plus_rent_indexcountry_encodedduration_yearsexchange_rategroceries_indexinsurance_usdlevelliving_cost_indexlocal_purchasing_power_indexno_of_indian_studentsoverall_scorepercentageprogramrankrent_indexrent_usdrestaurant_price_indextotal_costtuition_usdvisa_fee_usd
cost_of_living_index1.0000.9560.5460.033-0.1960.9480.5700.1660.6450.7190.5470.2150.5470.155-1.0000.8480.7800.7710.4450.4380.270
cost_of_living_plus_rent_index0.9561.0000.6780.069-0.2600.9170.6280.1720.6380.7430.6790.2700.6790.163-0.9560.9480.8220.7860.5830.5760.334
country_encoded0.5460.6781.0000.163-0.1320.6250.4150.1120.2190.4521.0000.2681.0000.244-0.5460.7880.6170.2980.8350.8510.359
duration_years0.0330.0690.1631.0000.0490.0390.0630.775-0.0070.0450.1630.0280.1630.000-0.0330.0980.0430.0170.4290.1150.085
exchange_rate-0.196-0.260-0.1320.0491.000-0.123-0.5360.032-0.477-0.289-0.133-0.179-0.1330.0000.196-0.341-0.379-0.531-0.235-0.244-0.365
groceries_index0.9480.9170.6250.039-0.1231.0000.5040.1300.5970.6920.6250.1770.6250.082-0.9480.8390.7610.6620.4600.4540.256
insurance_usd0.5700.6280.4150.063-0.5360.5041.0000.0820.7230.6620.4150.3180.4150.262-0.5700.6380.6660.7080.4570.4550.366
level0.1660.1720.1120.7750.0320.1300.0821.0000.1100.1620.1870.1410.1870.3070.1560.1780.0750.1600.3960.2330.172
living_cost_index0.6450.6380.219-0.007-0.4770.5970.7230.1101.0000.6920.2190.4240.2190.000-0.6450.5870.8270.7310.2760.2850.256
local_purchasing_power_index0.7190.7430.4520.045-0.2890.6920.6620.1620.6921.0000.4520.2790.4520.070-0.7190.7020.6660.7270.3960.4120.398
no_of_indian_students0.5470.6791.0000.163-0.1330.6250.4150.1870.2190.4521.0000.2681.0000.057-0.5470.7880.6170.2990.8350.8510.359
overall_score0.2150.2700.2680.028-0.1790.1770.3180.1410.4240.2790.2681.0000.2680.122-0.2150.2810.5810.3000.3750.4550.153
percentage0.5470.6791.0000.163-0.1330.6250.4150.1870.2190.4521.0000.2681.0000.057-0.5470.7880.6170.2990.8350.8510.359
program0.1550.1630.2440.0000.0000.0820.2620.3070.0000.0700.0570.1220.0571.0000.0600.1660.1160.2870.0000.1860.317
rank-1.000-0.956-0.546-0.0330.196-0.948-0.5700.156-0.645-0.719-0.547-0.215-0.5470.0601.000-0.848-0.780-0.771-0.445-0.438-0.270
rent_index0.8480.9480.7880.098-0.3410.8390.6380.1780.5870.7020.7880.2810.7880.166-0.8481.0000.7940.6910.6680.6590.340
rent_usd0.7800.8220.6170.043-0.3790.7610.6660.0750.8270.6660.6170.5810.6170.116-0.7800.7941.0000.7260.5920.6100.375
restaurant_price_index0.7710.7860.2980.017-0.5310.6620.7080.1600.7310.7270.2990.3000.2990.287-0.7710.6910.7261.0000.3680.3730.461
total_cost0.4450.5830.8350.429-0.2350.4600.4570.3960.2760.3960.8350.3750.8350.000-0.4450.6680.5920.3681.0000.9230.508
tuition_usd0.4380.5760.8510.115-0.2440.4540.4550.2330.2850.4120.8510.4550.8510.186-0.4380.6590.6100.3730.9231.0000.502
visa_fee_usd0.2700.3340.3590.085-0.3650.2560.3660.1720.2560.3980.3590.1530.3590.317-0.2700.3400.3750.4610.5080.5021.000

Missing values

2025-10-24T12:09:21.793284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-24T12:09:21.952212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-24T12:09:22.088082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

countrycityuniversityprogramlevelduration_yearstuition_usdliving_cost_indexrent_usdvisa_fee_usdinsurance_usdexchange_ratetotal_costrankcost_of_living_indexrent_indexcost_of_living_plus_rent_indexgroceries_indexrestaurant_price_indexlocal_purchasing_power_indexno_of_indian_studentspercentagecountry_encodedrank_2025overall_score
0United StatesCambridgeHarvard UniversityComputer ScienceMaster0.2500.8935480.5887950.8723400.2666671.0000000.0000200.3981539.00.6269740.6064810.7131660.6277290.6460810.7769151.0000001.0000001.00000040.959596
1United KingdomLondonImperial College LondonData ScienceMaster0.0000.6645160.5074000.7021280.9888890.4615380.0000150.14898522.00.5249090.4429010.5642630.4301310.5926370.6265260.1007370.1007370.93243220.981061
2CanadaTorontoUniversity of TorontoBusiness AnalyticsMaster0.2500.6209680.4725160.6170210.4333330.5384620.0000280.27618712.00.5589310.4753090.6034480.5862450.5807600.5627080.3013490.3013490.972973250.799242
3GermanyMunichTechnical University of MunichMechanical EngineeringMaster0.2500.0080650.4513740.4042550.0777780.2692310.0000180.01007721.00.5273390.3395060.5156740.4727070.4750590.6542730.0103300.0103300.743243280.787879
4NetherlandsAmsterdamUniversity of AmsterdamArtificial IntelligenceMaster0.0000.2548390.4799150.5744680.3111110.4000000.0000180.05942918.00.5382750.4799380.5924760.4628820.5748220.680355NaNNaNNaN550.667929
5FranceParisSorbonne UniversityInternational RelationsMaster0.2500.0725810.4947150.5319150.1311110.3461540.0000180.04002815.00.5455650.2870370.5031350.5676860.5166270.5554940.0301300.0301300.851351630.625000
6DenmarkCopenhagenUniversity of CopenhagenBioinformaticsMaster0.2500.0000000.4820300.4893620.1777780.1923080.0001610.0078287.00.6500610.3703700.6128530.5163760.8135390.6931190.0016450.0016450.4324321000.489899
7ChinaBeijingTsinghua UniversityComputer EngineeringMaster0.3750.1435480.2589850.2765960.2222220.1538460.0001670.08205485.00.1567440.1527780.1771160.2106990.0926370.3940070.1006110.1006110.918919200.829545
8AustriaViennaUniversity of ViennaSocial SciencesBachelor0.5000.0241940.4186050.3404260.2666670.2307690.0000180.02444511.00.5625760.3101850.5266460.5338430.5522570.5554940.0018020.0018020.4459461370.417929
9BelgiumBrusselsKU LeuvenBiomedical SciencesBachelor0.5000.0564520.4344610.3617020.3111110.2692310.0000180.04579923.00.5139730.2731480.4749220.4366810.6318290.5965590.0036100.0036100.554054630.625000
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359Saudi ArabiaTabukUniversity of TabukComputer EngineeringBachelor0.750.0661290.3742070.1829790.3555560.4615380.0000850.06464848.00.3219930.1373460.2805640.2631000.2149640.7358490.0051170.0051170.5810811001-1200NaN
360United StatesChicagoUniversity of ChicagoArtificial IntelligenceMaster0.250.9354840.5951370.7446810.2666671.0000000.0000200.4139469.00.6269740.6064810.7131660.6277290.6460810.7769151.0000001.0000001.000000210.825758
361United KingdomGlasgowUniversity of GlasgowData EngineeringPhD0.750.5483870.3879490.3404260.9888890.4615380.0000150.48132322.00.5249090.4429010.5642630.4301310.5926370.6265260.1007370.1007370.932432780.558081
362SpainSalamancaUniversity of SalamancaInformation SystemsMaster0.250.0500000.3625790.1914890.0888890.4230770.0000180.02520040.00.3462940.3163580.3840130.3111350.3717340.498335NaNNaNNaN5390.025253
363ItalyPisaUniversity of PisaSoftware DevelopmentMaster0.250.0532260.4344610.2978720.1777780.3846150.0000180.02825626.00.4544350.2592590.4294670.4312230.4809980.4217540.0070040.0070040.6891893820.125000
364United StatesSan FranciscoStanford UniversityData ScienceMaster0.250.8870970.7124740.9574470.2666671.0000000.0000200.3967809.00.6269740.6064810.7131660.6277290.6460810.7769151.0000001.0000001.00000060.950758
365United KingdomLeedsUniversity of LeedsComputer EngineeringMaster0.250.5645160.3742070.3191490.9888890.4615380.0000150.24786322.00.5249090.4429010.5642630.4301310.5926370.6265260.1007370.1007370.932432820.542929
366Saudi ArabiaAl-AhsaKing Faisal UniversityInformation SystemsMaster0.250.0677420.3847780.1914890.3555560.4615380.0000850.03333748.00.3219930.1373460.2805640.2631000.2149640.7358490.0051170.0051170.581081761-770NaN
367United StatesSeattleUniversity of WashingtonSoftware DevelopmentPhD1.000.8064520.5285410.7872340.2666671.0000000.0000200.8952869.00.6269740.6064810.7131660.6277290.6460810.7769151.0000001.0000001.000000760.561869
368United KingdomNottinghamUniversity of NottinghamData EngineeringMaster0.250.5483870.3530660.2765960.9888890.4615380.0000150.24031022.00.5249090.4429010.5642630.4301310.5926370.6265260.1007370.1007370.9324321080.476010